Performance Analysis of LSTMs for Daily Individual EV Charging Behavior Prediction

In this paper, we evaluate and analyze the performance of long short-term memory networks (LSTMs) for individual electric vehicle (EV) charging behavior prediction over the next day. The charging behavior consists of the charging duration level within a certain upper and lower range, the time slots...

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Autores principales:	Ahmed S. Khwaja, Bala Venkatesh, Alagan Anpalagan
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Electric vehicle charging prediction deep learning long short-term memory networks Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2c1b90ffa9264d209a2757be23306aea
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spelling	oai:doaj.org-article:2c1b90ffa9264d209a2757be23306aea2021-11-25T00:00:55ZPerformance Analysis of LSTMs for Daily Individual EV Charging Behavior Prediction2169-353610.1109/ACCESS.2021.3128491https://doaj.org/article/2c1b90ffa9264d209a2757be23306aea2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9615185/https://doaj.org/toc/2169-3536In this paper, we evaluate and analyze the performance of long short-term memory networks (LSTMs) for individual electric vehicle (EV) charging behavior prediction over the next day. The charging behavior consists of the charging duration level within a certain upper and lower range, the time slots in which charging will take place, the number of times charging will take place in each time slot, and whether the next day will be a charging day or not. Unlike existing work, we evaluate the behavior prediction performance for increasing resolutions of charging duration levels and charging time slots, using varying lengths of training data. The performance of the proposed approach is validated using real EV charging data, and comparison with other machine learning methods shows its generally superior prediction accuracy for all resolutions. We show that the best performance is achieved when around 8–10 months of data are used as training data. It is also shown that although the performance of the LSTMs degrades with increasing resolution, the performance for charging time slot prediction is affected less compared to that for charging duration prediction. We further propose, analyze and evaluate a new technique that improves the charging duration prediction performance.Ahmed S. KhwajaBala VenkateshAlagan AnpalaganIEEEarticleElectric vehiclecharging predictiondeep learninglong short-term memory networksElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154804-154814 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electric vehicle charging prediction deep learning long short-term memory networks Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electric vehicle charging prediction deep learning long short-term memory networks Electrical engineering. Electronics. Nuclear engineering TK1-9971 Ahmed S. Khwaja Bala Venkatesh Alagan Anpalagan Performance Analysis of LSTMs for Daily Individual EV Charging Behavior Prediction
description	In this paper, we evaluate and analyze the performance of long short-term memory networks (LSTMs) for individual electric vehicle (EV) charging behavior prediction over the next day. The charging behavior consists of the charging duration level within a certain upper and lower range, the time slots in which charging will take place, the number of times charging will take place in each time slot, and whether the next day will be a charging day or not. Unlike existing work, we evaluate the behavior prediction performance for increasing resolutions of charging duration levels and charging time slots, using varying lengths of training data. The performance of the proposed approach is validated using real EV charging data, and comparison with other machine learning methods shows its generally superior prediction accuracy for all resolutions. We show that the best performance is achieved when around 8–10 months of data are used as training data. It is also shown that although the performance of the LSTMs degrades with increasing resolution, the performance for charging time slot prediction is affected less compared to that for charging duration prediction. We further propose, analyze and evaluate a new technique that improves the charging duration prediction performance.
format	article
author	Ahmed S. Khwaja Bala Venkatesh Alagan Anpalagan
author_facet	Ahmed S. Khwaja Bala Venkatesh Alagan Anpalagan
author_sort	Ahmed S. Khwaja
title	Performance Analysis of LSTMs for Daily Individual EV Charging Behavior Prediction
title_short	Performance Analysis of LSTMs for Daily Individual EV Charging Behavior Prediction
title_full	Performance Analysis of LSTMs for Daily Individual EV Charging Behavior Prediction
title_fullStr	Performance Analysis of LSTMs for Daily Individual EV Charging Behavior Prediction
title_full_unstemmed	Performance Analysis of LSTMs for Daily Individual EV Charging Behavior Prediction
title_sort	performance analysis of lstms for daily individual ev charging behavior prediction
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/2c1b90ffa9264d209a2757be23306aea
work_keys_str_mv	AT ahmedskhwaja performanceanalysisoflstmsfordailyindividualevchargingbehaviorprediction AT balavenkatesh performanceanalysisoflstmsfordailyindividualevchargingbehaviorprediction AT alagananpalagan performanceanalysisoflstmsfordailyindividualevchargingbehaviorprediction
_version_	1718414686232248320

Performance Analysis of LSTMs for Daily Individual EV Charging Behavior Prediction

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